{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Basic autograd example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor(1., requires_grad=True)\n",
    "w = torch.tensor(2., requires_grad=True)\n",
    "b = torch.tensor(3., requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = w * x + b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(2.)\n",
      "tensor(1.)\n",
      "tensor(1.)\n"
     ]
    }
   ],
   "source": [
    "print(x.grad)\n",
    "print(w.grad)\n",
    "print(b.grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic autograd example 2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w:  Parameter containing:\n",
      "tensor([[-0.2193, -0.4749,  0.5237],\n",
      "        [ 0.3885,  0.4380,  0.4996]], device='cuda:0', requires_grad=True)\n",
      "b:  Parameter containing:\n",
      "tensor([-0.4298,  0.0819], device='cuda:0', requires_grad=True)\n",
      "loss:  2.5752384662628174\n",
      "dL/dw:  tensor([[-0.3993, -0.6786,  0.3169],\n",
      "        [ 0.5113,  0.7795,  1.4789]], device='cuda:0')\n",
      "dL/db:  tensor([-1.1608,  1.1440], device='cuda:0')\n",
      "loss after 1 step optimization:  2.5113987922668457\n"
     ]
    }
   ],
   "source": [
    "x = torch.randn(10, 3).cuda()\n",
    "y = torch.randn(10, 2).cuda()\n",
    "\n",
    "linear = nn.Linear(3, 2).cuda()\n",
    "print('w: ', linear.weight)\n",
    "print('b: ', linear.bias)\n",
    "\n",
    "criterion = nn.MSELoss().cuda()\n",
    "optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)\n",
    "\n",
    "pred = linear(x)\n",
    "loss = criterion(pred, y)\n",
    "print('loss: ', loss.item())\n",
    "\n",
    "loss.backward()\n",
    "\n",
    "print('dL/dw: ',linear.weight.grad)\n",
    "print('dL/db: ', linear.bias.grad)\n",
    "# 1-step gradient descent\n",
    "optimizer.step()\n",
    "\n",
    "# You can also perform gradient descent at the low level.\n",
    "# linear.weight.data.sub_(0.01 * linear.weight.grad.data)\n",
    "# linear.bias.data.sub_(0.01 * linear.bias.grad.data)\n",
    "\n",
    "# Print out the loss after 1-step gradient descent \n",
    "pred = linear(x)\n",
    "loss = criterion(pred, y)\n",
    "print('loss after 1 step optimization: ', loss.item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Loading data from numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建一个numpy矩阵\n",
    "x = np.array([[1, 2],[3, 4]])\n",
    "\n",
    "#将numpy矩阵转为torch张量\n",
    "y = torch.from_numpy(x).cuda()\n",
    "\n",
    "#将torch张量转为numpy矩阵\n",
    "z = y.cpu().numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Input pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "# 下载和构造 CIFRA-10 数据集\n",
    "train_dataset = torchvision.datasets.CIFAR10(root='../../data/',\n",
    "                                            train=True,\n",
    "                                            transform=transforms.ToTensor(),\n",
    "                                            download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32])\n",
      "<class 'torch.Tensor'>\n",
      "1\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# 获取数据集中的一对数据（从本地磁盘中获取数据）\n",
    "image, label = train_dataset[32]\n",
    "print(image.size())\n",
    "print(type(image))\n",
    "print(label)\n",
    "print(type(label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 32, 32])\n"
     ]
    },
    {
     "data": {
      "image/png": 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NMD4Mxc2Nn/5djLd4UfDPL4UEV/rhozeBbQZh1cR9RoqDb190B/gseepLYAvcdBnYnqmf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\nuqIoikvQBV1RFMUl/A+Q0YvROEZMqQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e00c7c400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#显示这一对数据看看~\n",
    "import matplotlib.pyplot as plt\n",
    "img = image.numpy()\n",
    "print(image.shape)\n",
    "#数据为(3 ,32 ,32)，尝试两种方法来转为(32, 32, 3)\n",
    "plt.figure()\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(np.swapaxes(img, 0, 2))#尝试第一种方法，正确！\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(img.reshape(32,32,3))#尝试第二种方法，错了\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用数据生成器（DataLoader）\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                           batch_size=64,\n",
    "                                           shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([64, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "#当迭代开始时，队列和线程开始从文件（数据集）中加载数据\n",
    "data_iter = iter(train_loader)\n",
    "\n",
    "# 一个batch\n",
    "images, labels = data_iter.next()\n",
    "\n",
    "print(images.size())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B//RP+Js//RcpqxnHR3ep69sYo7l54w3uA7GpOXP2AlsrW7z6Ry+z0u3yxs3r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YWv/gmTowmBCwiDiHY7pdma0bBtbu7epsgruss9slmOEILu05vE7Ygghka7SSUMv/TL/5rXr97A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\nIq+8dJZauYyQgsxoGBYtY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e001ab278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pic = np.zeros((32*8, 32*8, 3))\n",
    "row = 0\n",
    "col = 0\n",
    "row_flag = 0\n",
    "col_flag = 0\n",
    "i = 0\n",
    "for image, label in zip(images, labels):\n",
    "    # 训练代码写此处\n",
    "    img = np.swapaxes(image.numpy(), 0, 2)\n",
    "    #print(image.shape)\n",
    "    #break\n",
    "    pic[row*32:(row+1)*32, col*32:(col+1)*32, :] = img\n",
    "    i += 1\n",
    "    if i!= 8:#画图次数\n",
    "        col += 1\n",
    "    else:\n",
    "        row += 1\n",
    "        col = 0\n",
    "        i = 0\n",
    "        \n",
    "    # pass\n",
    "plt.imshow(pic)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input pipelines for custom dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建自定义的数据集，应该以如下格式：\n",
    "class CustomDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self):\n",
    "        # TODO(代办事项)\n",
    "        # 构建初始化文件路径 或者 文件名列表\n",
    "        pass\n",
    "    def __getitem__(self, index):\n",
    "        # TODO\n",
    "        # 1. 从文件中读取一个数据 (e.g. using numpy.formfile, PIL.Image.open)\n",
    "        # 2. 对这个数据进行预处理 (e.g. torchvision.Transform)\n",
    "        # 3. 返回数据对 (e.g. image and label)\n",
    "        pass\n",
    "    def __len__(self):\n",
    "        # 这里应该改变0为我的数据集的整个的大小\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 我可以使用预创建的数据生成器(DataLoader)\n",
    "custom_dataset = CustomDataset()\n",
    "train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,\n",
    "                                          batch_size=64,\n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [实例1参考链接](https://pytorch.org/docs/master/_modules/torchvision/datasets/mnist.html#FashionMNIST)\n",
    "以下是自定义数据集实例1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import torch.utils.data as data\n",
    "from PIL import Image\n",
    "import os\n",
    "import os.path\n",
    "import gzip\n",
    "import numpy as np\n",
    "import torch\n",
    "import codecs\n",
    "from .utils import download_url, makedir_exist_ok\n",
    "\n",
    "\n",
    "class MNIST(data.Dataset):\n",
    "    \"\"\"`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset.\n",
    "\n",
    "    Args:\n",
    "        root (string): Root directory of dataset where ``processed/training.pt``\n",
    "            and  ``processed/test.pt`` exist.\n",
    "        train (bool, optional): If True, creates dataset from ``training.pt``,\n",
    "            otherwise from ``test.pt``.\n",
    "        download (bool, optional): If true, downloads the dataset from the internet and\n",
    "            puts it in root directory. If dataset is already downloaded, it is not\n",
    "            downloaded again.\n",
    "        transform (callable, optional): A function/transform that  takes in an PIL image\n",
    "            and returns a transformed version. E.g, ``transforms.RandomCrop``\n",
    "        target_transform (callable, optional): A function/transform that takes in the\n",
    "            target and transforms it.\n",
    "    \"\"\"\n",
    "    urls = [\n",
    "        'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',\n",
    "        'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',\n",
    "        'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',\n",
    "        'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',\n",
    "    ]\n",
    "    training_file = 'training.pt'\n",
    "    test_file = 'test.pt'\n",
    "    classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',\n",
    "               '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']\n",
    "\n",
    "    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):\n",
    "        self.root = os.path.expanduser(root)\n",
    "        self.transform = transform\n",
    "        self.target_transform = target_transform\n",
    "        self.train = train  # training set or test set\n",
    "\n",
    "        if download:\n",
    "            self.download()\n",
    "\n",
    "        if not self._check_exists():\n",
    "            raise RuntimeError('Dataset not found.' +\n",
    "                               ' You can use download=True to download it')\n",
    "\n",
    "        if self.train:\n",
    "            data_file = self.training_file\n",
    "        else:\n",
    "            data_file = self.test_file\n",
    "        self.data, self.targets = torch.load(os.path.join(self.processed_folder, data_file))\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            index (int): Index\n",
    "\n",
    "        Returns:\n",
    "            tuple: (image, target) where target is index of the target class.\n",
    "        \"\"\"\n",
    "        img, target = self.data[index], int(self.targets[index])\n",
    "\n",
    "        # doing this so that it is consistent with all other datasets\n",
    "        # to return a PIL Image\n",
    "        img = Image.fromarray(img.numpy(), mode='L')\n",
    "\n",
    "        if self.transform is not None:\n",
    "            img = self.transform(img)\n",
    "\n",
    "        if self.target_transform is not None:\n",
    "            target = self.target_transform(target)\n",
    "\n",
    "        return img, target\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    @property\n",
    "    def raw_folder(self):\n",
    "        return os.path.join(self.root, self.__class__.__name__, 'raw')\n",
    "\n",
    "    @property\n",
    "    def processed_folder(self):\n",
    "        return os.path.join(self.root, self.__class__.__name__, 'processed')\n",
    "\n",
    "    @property\n",
    "    def class_to_idx(self):\n",
    "        return {_class: i for i, _class in enumerate(self.classes)}\n",
    "\n",
    "    def _check_exists(self):\n",
    "        return os.path.exists(os.path.join(self.processed_folder, self.training_file)) and \\\n",
    "            os.path.exists(os.path.join(self.processed_folder, self.test_file))\n",
    "\n",
    "    @staticmethod\n",
    "    def extract_gzip(gzip_path, remove_finished=False):\n",
    "        print('Extracting {}'.format(gzip_path))\n",
    "        with open(gzip_path.replace('.gz', ''), 'wb') as out_f, \\\n",
    "                gzip.GzipFile(gzip_path) as zip_f:\n",
    "            out_f.write(zip_f.read())\n",
    "        if remove_finished:\n",
    "            os.unlink(gzip_path)\n",
    "\n",
    "    def download(self):\n",
    "        \"\"\"Download the MNIST data if it doesn't exist in processed_folder already.\"\"\"\n",
    "\n",
    "        if self._check_exists():\n",
    "            return\n",
    "\n",
    "        makedir_exist_ok(self.raw_folder)\n",
    "        makedir_exist_ok(self.processed_folder)\n",
    "\n",
    "        # download files\n",
    "        for url in self.urls:\n",
    "            filename = url.rpartition('/')[2]\n",
    "            file_path = os.path.join(self.raw_folder, filename)\n",
    "            download_url(url, root=self.raw_folder, filename=filename, md5=None)\n",
    "            self.extract_gzip(gzip_path=file_path, remove_finished=True)\n",
    "\n",
    "        # process and save as torch files\n",
    "        print('Processing...')\n",
    "\n",
    "        training_set = (\n",
    "            read_image_file(os.path.join(self.raw_folder, 'train-images-idx3-ubyte')),\n",
    "            read_label_file(os.path.join(self.raw_folder, 'train-labels-idx1-ubyte'))\n",
    "        )\n",
    "        test_set = (\n",
    "            read_image_file(os.path.join(self.raw_folder, 't10k-images-idx3-ubyte')),\n",
    "            read_label_file(os.path.join(self.raw_folder, 't10k-labels-idx1-ubyte'))\n",
    "        )\n",
    "        with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:\n",
    "            torch.save(training_set, f)\n",
    "        with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:\n",
    "            torch.save(test_set, f)\n",
    "\n",
    "        print('Done!')\n",
    "\n",
    "    def __repr__(self):\n",
    "        fmt_str = 'Dataset ' + self.__class__.__name__ + '\\n'\n",
    "        fmt_str += '    Number of datapoints: {}\\n'.format(self.__len__())\n",
    "        tmp = 'train' if self.train is True else 'test'\n",
    "        fmt_str += '    Split: {}\\n'.format(tmp)\n",
    "        fmt_str += '    Root Location: {}\\n'.format(self.root)\n",
    "        tmp = '    Transforms (if any): '\n",
    "        fmt_str += '{0}{1}\\n'.format(tmp, self.transform.__repr__().replace('\\n', '\\n' + ' ' * len(tmp)))\n",
    "        tmp = '    Target Transforms (if any): '\n",
    "        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\\n', '\\n' + ' ' * len(tmp)))\n",
    "        return fmt_str\n",
    "\n",
    "\n",
    "\n",
    "class FashionMNIST(MNIST):\n",
    "    \"\"\"`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset.\n",
    "\n",
    "    Args:\n",
    "        root (string): Root directory of dataset where ``processed/training.pt``\n",
    "            and  ``processed/test.pt`` exist.\n",
    "        train (bool, optional): If True, creates dataset from ``training.pt``,\n",
    "            otherwise from ``test.pt``.\n",
    "        download (bool, optional): If true, downloads the dataset from the internet and\n",
    "            puts it in root directory. If dataset is already downloaded, it is not\n",
    "            downloaded again.\n",
    "        transform (callable, optional): A function/transform that  takes in an PIL image\n",
    "            and returns a transformed version. E.g, ``transforms.RandomCrop``\n",
    "        target_transform (callable, optional): A function/transform that takes in the\n",
    "            target and transforms it.\n",
    "    \"\"\"\n",
    "    urls = [\n",
    "        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz',\n",
    "        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz',\n",
    "        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz',\n",
    "        'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz',\n",
    "    ]\n",
    "    classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',\n",
    "               'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "# 直接丢链接用迅雷下的，所以得使用源码，供于学习\n",
    "# from __future__ import print_function\n",
    "# import torch.utils.data as data\n",
    "# from PIL import Image\n",
    "# import os\n",
    "# import os.path\n",
    "# import gzip\n",
    "# import numpy as np\n",
    "# import torch\n",
    "# import codecs\n",
    "# def get_int(b):\n",
    "#     return int(codecs.encode(b, 'hex'), 16)\n",
    "\n",
    "# def read_label_file(path):\n",
    "#     with open(path, 'rb') as f:\n",
    "#         data = f.read()\n",
    "#         assert get_int(data[:4]) == 2049\n",
    "#         length = get_int(data[4:8])\n",
    "#         parsed = np.frombuffer(data, dtype=np.uint8, offset=8)\n",
    "#         return torch.from_numpy(parsed).view(length).long()\n",
    "\n",
    "\n",
    "# def read_image_file(path):\n",
    "#     with open(path, 'rb') as f:\n",
    "#         data = f.read()\n",
    "#         assert get_int(data[:4]) == 2051\n",
    "#         length = get_int(data[4:8])\n",
    "#         num_rows = get_int(data[8:12])\n",
    "#         num_cols = get_int(data[12:16])\n",
    "#         parsed = np.frombuffer(data, dtype=np.uint8, offset=16)\n",
    "#         return torch.from_numpy(parsed).view(length, num_rows, num_cols)\n",
    "\n",
    "#     print('Processing...')\n",
    "# raw_folder = '../../data/fashion/raw'\n",
    "# training_file = 'training.pt'\n",
    "# test_file = 'test.pt'\n",
    "# processed_folder = '../../data/fashion/processed'\n",
    "\n",
    "# training_set = (\n",
    "#             read_image_file(os.path.join(raw_folder, 'train-images-idx3-ubyte')),\n",
    "#             read_label_file(os.path.join(raw_folder, 'train-labels-idx1-ubyte'))\n",
    "#         )\n",
    "# test_set = (\n",
    "#             read_image_file(os.path.join(raw_folder, 't10k-images-idx3-ubyte')),\n",
    "#             read_label_file(os.path.join(raw_folder, 't10k-labels-idx1-ubyte'))\n",
    "#         )\n",
    "# with open(os.path.join(processed_folder, training_file), 'wb') as f:\n",
    "#             torch.save(training_set, f)\n",
    "# with open(os.path.join(processed_folder, test_file), 'wb') as f:\n",
    "#             torch.save(test_set, f)\n",
    "# print('Done!')\n",
    "pretrained_dataset = torchvision.datasets.FashionMNIST('../../data/fashion', train=True, transform=None, target_transform=None, download=False)\n",
    "#如果要想MNIST一样，则用transform=transforms.ToTensor()\n",
    "train_loader = torch.utils.data.DataLoader(dataset=pretrained_dataset,\n",
    "                                          batch_size=1,\n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'PIL.Image.Image'>\n",
      "<class 'torch.Tensor'>\n"
     ]
    }
   ],
   "source": [
    "image, label = pretrained_dataset[32]\n",
    "print(type(image))\n",
    "print(type(label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "pretrained_dataset = torchvision.datasets.FashionMNIST('../../data/fashion', train=True, transform=np.asarray, target_transform=None, download=False)\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=pretrained_dataset,\n",
    "                                          batch_size=1,\n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "img.shape:  (28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x18e00c4a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "target:  9\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "img, label = pretrained_dataset[0]\n",
    "print('img.shape: ',img.shape)\n",
    "plt.figure()\n",
    "plt.imshow(img)#尝试第二种方法，错了\n",
    "plt.show()\n",
    "print('target: ', label.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [实例2参考链接](https://blog.csdn.net/u012684062/article/details/78547505)\n",
    "以下是自定义数据集实例2："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyTrainData(torch.utils.data.Dataset):\n",
    "    def __init__(self):\n",
    "        self.video_path = '/data/FrameFeature/Penn/'\n",
    "        self.video_file = '/data/FrameFeature/Penn_train.txt'\n",
    "        fp = open(self.video_file,'r')\n",
    "        lines = fp.readlines()\n",
    "        fp.close()\n",
    "        self.video_name = []\n",
    "        for line in lines:\n",
    "            self.video_name.append(line.strip().split(' ')[0])\n",
    "    def __len__(self):\n",
    "        return len(self.video_name)\n",
    "    def __getitem__(self, index):\n",
    "        data = load_feature(os.path.join(self.video_path,self.video_name[index]))\n",
    "        data = np.expand_dims(data,2)\n",
    "        return data\n",
    "\n",
    "custom_dataset = CustomDataset()\n",
    "train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,\n",
    "                                          batch_size=1,\n",
    "                                          shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pretrained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Download and load the pretrained Resnet-18\n",
    "resnet = torchvision.models.resnet18(pretrained=True).cuda()\n",
    "# 保存和加载整个模型\n",
    "torch.save(resnet, 'model_ch1.ckpt')\n",
    "model = torch.load('model_ch1.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 仅保存和加载 模型的参数（推荐）\n",
    "torch.save(resnet.state_dict(), 'params.ckpt')\n",
    "resnet.load_state_dict(torch.load('params.ckpt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow-gpu",
   "language": "python",
   "name": "tensorflow-gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
